A practical, milestone-based roadmap built from real engagements with engineering teams in Thailand. Pick a track — including how to bring AI into your Platform and DevSecOps workflows — follow the stages and ship with confidence.
Choosing the right path saves months of trial and error. Each track below is built from real Thai enterprise projects — DevOps for delivery speed, DevSecOps for security-by-default, Platform Engineering for scaling developer experience, and AI for Platform & DevSecOps for teams who want to amplify every engineer with LLMs, AIOps and security copilots.
Follow the milestones below — each stage builds on the previous one.
Master Linux, networking, and shell scripting — the bedrock for everything that follows.
Automate builds, tests, and deployments with modern pipeline tooling.
Containerize workloads and orchestrate them at scale with Kubernetes.
Provision and version every layer of your infrastructure with code.
Measure what matters — metrics, logs, traces, SLO and incident response.
Build internal platforms on AWS / GCP / Azure that engineers love to use.
Browse the courses that match each stage above — or talk to our team to customize the path for your team.
Quick answers about choosing and following a DevOps, DevSecOps, Platform Engineering or AI-augmented engineering path.
DevOps focuses on automating delivery between Dev and Ops. DevSecOps extends DevOps by embedding security into every stage of the pipeline. Platform Engineering builds an internal product — a developer platform — so application teams can self-serve infrastructure, CI/CD and observability through golden paths.
Most learners reach a junior DevOps level in 4–6 months by following the foundation, CI/CD and containers stages. Reaching mid-senior with cloud, IaC and observability typically takes 12–18 months of consistent practice on real projects.
Yes. DevSecOps assumes you already understand CI/CD, containers and infrastructure as code. Finish the DevOps foundation and CI/CD stages first, then add SAST/DAST, secrets management and policy as code from the DevSecOps track.
No. DevOps is a culture and set of practices. Platform Engineering is a discipline that builds an Internal Developer Platform (IDP) — a real product with users, golden paths, self-service infra and a developer portal — to make those DevOps practices easy to adopt at scale.
Start with Git, Linux, Docker and a CI/CD tool like GitHub Actions or GitLab CI. Then add Kubernetes, Terraform and Prometheus. For DevSecOps add Trivy, Snyk and OPA. For Platform Engineering add Backstage and Crossplane.
Yes. CodeNgaiNgai offers DevOps, DevSecOps, Platform Engineering and AI for Engineering courses in Thai with hands-on workshops, taught by engineers who have built these systems for real enterprises.
Start with AI-assisted coding (Copilot, Claude Code, Cursor) to ship faster, then add AI-driven vulnerability triage and threat detection to your DevSecOps pipeline. For Platform Engineering, integrate LLM agents into Backstage, expose natural-language interfaces to your IaC and use RAG over your runbooks. The AI for Platform & DevSecOps track walks through each of these step by step.
AIOps applies machine learning and LLMs to operational data — metrics, logs, traces and alerts — to detect anomalies, cluster noisy alerts and even auto-remediate incidents. Platform teams care because it directly improves SLOs, reduces on-call fatigue and lets a small team operate a much larger fleet of services.
No. The AI for Platform & DevSecOps track is built for engineers who already know DevOps fundamentals. You will learn LLMs, RAG, prompt engineering, security copilots and AIOps patterns — not how to train foundation models from scratch.
